“We are excited to identify a new, multidisciplinary application for our high-quality climate data services. Malaria is one of India’s most pressing health concerns.
By combining meteorological information with the information from the health sector, we can examine micro-trends and predict malaria patterns, and accelerate our national progress towards its elimination.
The Interagency Expert Committee announced today will function as a highly effective platform to accomplish this objective,” said Mrutyunjay Mohapatra, Director General of Meteorology, IMD, Government of India, in a statement.
The ICMR will provide the scientific rigour quintessential to develop robust and scalable use cases to combat the vector borne diseases.
“The impact of climate change has emerged as a major threat to the very existence of the human race. At the ICMR, we are working to combat the climate-sensitive diseases through a concerted national strategic action focused on climate and health. Vector borne diseases like Malaria are priority targets for action in the health-climate agenda.
The relations between these disease and the climate variables have been extensively studied in the past, and it is time to apply these evidences to design programmatic solutions to accelerate India’s progress against these diseases,” added Manju Rahi, Scientist F and Deputy Director General of Epidemiology and Communicable Diseases, ICMR, Government of India.
The IEC’s launch is part of a global initiative — Forecasting Healthy Futures — to develop weather data-informed strategies and policies to improve health outcomes and accelerate progress against malaria and other deadly mosquito-borne diseases.
The IEC will focus on improving models like the one Malaria No More developed in Odisha state, which uses a multi-stakeholders and interdisciplinary approach to identify and prioritise areas of scalable and sustainable impact to improve malaria control and prevention.
Malaria No More’s weather-based prediction model is designed to produce data-driven solutions to guide planning of national malaria prevention campaigns, test and treatment interventions, advanced positioning of medical products, and the deployment of community health workers.
The forecasting model uses advanced weather data, health information, and deep learning algorithms, and produces practical visualisation outputs for local decision-making in the pilot districts of Koraput and Malkangiri in Odisha.
Source: IANS
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